The subcellular distribution of synapses is critical for the assembly, function, and plasticity of the nervous system and plays a role in its disorders. Underlying molecular mechanisms, however, remain largely unknown. While advanced multidimensional images, in conjunction with single-cell genetic techniques, have afforded an unprecedented opportunity to understand synapse development at a new level, there is a knowledge gap in our capacity to effectively quantify subcellular synapses from large quantities of three-dimensional images. This is a significant problem and has hampered large-scale studies of the molecular mechanisms of synapse development, especially in neurons with complex arbor-such as Purkinje cells in mammals and lobula plate tangential cells (LPTC) in Drosophila-where existing approaches do not yield complete or robust synapse quantification for the entire dendritic tree and do not scale to efficient genetic screening. The objective of thi project is to bridge this gap by providing tools for quantitative investigation of subcellular synapse distribution and its molecular mechanisms using three-dimensional microscopy images. Specifically, our highly cross- disciplinary team will pursue two aims: (1) Develop automatic algorithms to analyze and quantify synapse distribution in the entire dendritic tree of neurons with complex arbor. Holistic and objective description of synapse density will enable automatic detection of mutant patterns. (2) Develop automatic algorithms to analyze and quantify synapse distribution in different parts of the entire dendritic tree of neurons with complex arbor. Efficient quantification at distinct subcellular locations will assist discovery of novel regulators for different subcellular parts. As a test case, we will use synapse distribution n Drosophila LPTC neurons, which are amenable to both genome-wide genetic screens and genetic manipulations with single-neuron resolution. We will develop reliable methods to characterize the density of inhibitory GABAergic and excitatory cholinergic synapses from three-dimensional fluorescence confocal images. Our algorithms will lead to the next level of mechanistic understanding that controls the subcellular distribution of inhibitory and excitatory synapses, and enable a wide range of quantitative analyses for other types of neurons with similar complexity. Powerful multichannel co-analysis and machine learning approaches will be used to improve synapse detection and subcellular compartment extraction for overcoming challenges in 3D confocal image, including staining artifacts and anisotropic resolution. Algorithms will be developed using a model-guided methodology that emphasizes efficiency for large volume 3D images during genetic screening. Pattern-recognition methods will be used to speed up proofreading of the synapse quantification results. A novel ordering strategy will be adapted for neurons of complex dendritic arbor to quantify subcellular synapses in a functionally meaningful way. The project will produce a set of open-source, extensible tools for automatic synapse quantification and proofreading, with friendly graphical-user interfaces, to serve the neuroscience community.